# bivariate.mixalg: EM algorithm and classification for univariate data, for... In CAMAN: Finite Mixture Models and Meta-Analysis Tools - Based on C.A.MAN

Function

## Usage

 ```1 2 3``` ```bivariate.mixalg(obs1, obs2, type, data = NULL, var1, var2, corr, lambda1, lambda2, p,startk, numiter=5000, acc=1.e-7, class) ```

## Arguments

 `obs1` the first column of the observations

 `obs2` the second column of the observations

 `type` kind of data

 `data` an optional data frame

 `var1` Variance of the first column of the observations(except meta-analysis)

 `var2` Variance of the second column of the observations (except meta-analysis)

 `corr` correlation coefficient

 `lambda1` Means of the first column of the observations

 `lambda2` Means of the second column of the observations

 `p` Probability

 `startk` starting/maximal number of components. This number will be used to compute the grid in the VEM. Default is 20.

 `numiter` parameter to control the maximal number of iterations in the VEM and EM loops. Default is 5000.

 `acc` convergence criterion. Default is 1.e-7

 `class` classification of studies

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```## Not run: #1.EM and classification for bivariate data #Examples data(rs12363681) test <- bivariate.mixalg(obs1=x, obs2=y, type="bi", lambda1=0, lambda2=0, p=0, data=rs12363681, startk=20, class="TRUE") #scatter plot with ellipse plot(test) #scatter plot without ellipse plot(test, ellipse = FALSE) #2.EM and classification for meta data #Examples data(CT) bivariate.mixalg(obs1=logitTPR, obs2=logitTNR, var1=varlogitTPR, var2=varlogitTNR, type="meta", lambda1=0, lambda2=0, p=0,data=CT,startk=20,class="TRUE") ## End(Not run) ```

CAMAN documentation built on May 1, 2019, 9:21 p.m.